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References
Some of the most relevant references for this project are presented here
[1] K. Barnard, P. Duygulu, N. Freitas, D. Forsyth, D. Blei, and M. I. Jordan, Matching Words and Pictures, Journal of Machine Learning Research, Vol. 3, pp. 1107-1135, 2003.
[2] G. Csurka, C.R. Dance, C. Bray, L. Fan and J. Willamowski, Visual Categorization with Bags of Keypoints, PASCAL Pattern Recognition andMachine Learning in ComputerVisionWorkshop, Grenoble, May 2004.
[3] G. Dorko and C. Schmid, Selection of scale invariant neighborhoods for object class recognition. International Conference on Computer Vision, 2003.
[4] K. Barnard, P. Duygulu, N. Freitas, D. Forsyth, D. Blei, and M. I. Jordan, Matching Words and Pictures, Journal of Machine Learning Research, Vol. 3, pp. 1107-1135, 2003.
[5] T. Goedeme, T. Tuytelaars, and L. Van Gool, Fast Wide Baseline Matching for Visual Navigation Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2004.
[6] T. Hofmann, Unsupervised Learning by Probabilistic Latent Semantic Analysis, Machine Learning, Vol. 42, pp. 177-196, 2001.
[7] M. Keller and S. Bengio, Theme Topic Mixture Model: A Graphical Model for Document Representation, IDIAP Research Report, IDIAP-RR-04-05, Jan. 2004.
[8] K. Mikolajczyk and C. Schmid, An Affine Invariant Interest Point Detector, European Conference on Computer Vision, 2002.
[9] F. Monay and D. Gatica-Perez, On Image Auto-Annotation with Latent Space Models, Proceedings ACM Int. Conf. on Multimedia (ACM MM), 2003.
[10] F. Monay and D. Gatica-Perez, PLSA-based Image Auto-Annotation: Constraining the Latent Space, IDIAP Research Report, IDIAP-RR-04-30,May 2004.
[11] A. Opelt, M- Fussenegger, A. Pinz and P. Auer, Weak hypotheses and Boosting for Generic Object Detection and Recognition, Europ. Conf. Computer Vision, Prague, May 2004.
[12] M. Osian, T. Tuytelaars, and L. Van Gool, Fitting Superellipses to Incomplete Contours,Workshop on Perceptual Organization in Computer Vision, 2004.
[13] P. Quelhas and J.-M. Odobez, A Color and Gradient Local Descriptor Fusion Scheme For Object Recognition IEEEWIAMIS workshop, 2004.
[14] J. Sivic and A. Zisserman, Video Google: A Text Retrieval Approach to Object Matching in Videos, Proceedings International Conference on Computer Vision,2003.
[15] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Trans. on PAMI, Vol. 22, No. 12, pp. 1349-1380, Dec. 2000.
[16] T. Tuytelaars and L. Van Gool, Content-based Image Retrieval based on Local Affinely Invariant Regions Third Int l Conf. on Visual Information Systems, Visual99, pp. 493-500, 1999.
[17] T. Tuytelaars and L. Van Gool Wide Baseline Stereo based on Local, Affinely invariant Regions, British Machine Vision Conference, pp. 412-422, 2000.
[18] A. Vailaya, A. Jain, and H.J. Zhang, On Image Classification: City Images vs. Landscapes, Pattern Recognition, Vol. 31, pp. 1921-1936, Dec. 1998.
[19] A. Vailaya, M. Figueiredo, A. Jain, and H.J. Zhang, Image Classification for Content-based Indexing, IEEE Trans. on Image Processing, Vol. 10, No. 1, pp. 117-130, Jan. 2001.
[20] A. Vinciarelli, Noisy Text Categorization, in Proc. Int. Conf. on Pattern Recognition, Jul. 2004.
[21] L. Zhu, A. Rao, and A. Zhang, Theory of Keyblock-based image retrieval, ACM Transaction on Information Systems, 20(2):pp.224-257, 2002.
[22] D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91 110, 2004.
[23] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. In Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, Toronto, Jun. 2003.
[24] R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.
[25] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121 167, 1998.